Taniya Kapoor

Taniya Kapoor

Assistant Professor

Publications

Preprints

  1. Abhishek Chandra*, Taniya Kapoor*. Oscillatory State-Space Models as Inductive Biases for Physics-Informed Neural PDE Solvers (2026)
  2. R. Gardiner, H. Cerbone, H. A. Akande, C. Batist, A. Cowans, S. Felsinger, P. Gill, Taniya Kapoor, E. Miller, R. Oyanedel, S. Reece, Y. Ying Tan, J. Antony, C. Rodriguez-Pardo, A. Hinsley, M. Bowles, S. Heilpern, R. Parkinson. Towards Ecologically Meaningful Foundation Models (2026)
  3. Taniya Kapoor*, Hongrui Wang*, Alfredo Núñez, Rolf Dollevoet. Extrapolating Railway Catenary Dynamics to Unobserved Operational Conditions Using Long Expressive Memory (2025)
  4. Abhishek Chandra, Taniya Kapoor, Mitrofan Curti, Koen Tiels, Elena A. Lomonova. Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems (2025)
  5. Taniya Kapoor*, Abhishek Chandra*, Barbara Rossi, Stephen J. Roberts, Hongrui Wang, Alfredo Núñez. Holistic AI Will Revolutionize Structural Engineering: From Strength to Sustainability (2025)
  6. Taniya Kapoor*, Abhishek Chandra*, Anastasios Stamou, Stephen J. Roberts. Beyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solvers (2025)

Journal Papers

  1. Yingjie Shao, Ioannis N. Athanasiadis, George van Voorn, Taniya Kapoor. Curvature-Aware Dynamic Precision Approach for Physics-Informed Neural Networks Neurocomputing (2026)
  2. Anastasios Stamou, Taniya Kapoor, M. Fragiadakis. A Unified Enhanced Separable PINN Framework for Forward and Inverse Dynamic Analysis of Beams and Plates Engineering Applications of Artificial Intelligence (2026)
  3. Abhishek Chandra*, Taniya Kapoor*, Bram Daniels, Mitrofan Curti, Koen Tiels, Daniel M. Tartakovsky, Elena A. Lomonova. Generalizable models of magnetic hysteresis via physics-aware recurrent neural networks Computer Physics Communications (2025)
  4. Taniya Kapoor, Hongrui Wang, Anastasios Stamou, Kareem El Sayed, Alfredo Núñez, Daniel M. Tartakovsky, Rolf Dollevoet. Neural differential equation-based two-stage approach for generalization of beam dynamics IEEE Transactions on Industrial Informatics (2024)
  5. Abhishek Chandra, Taniya Kapoor, Mitrofan Curti, Koen Tiels, Elena A. Lomonova. Characterizing nonlinear piezoelectric dynamics through deep neural operator learning Applied Physics Letters (2024)
  6. Taniya Kapoor, Hongrui Wang, Alfredo Núñez, Rolf Dollevoet. Transfer learning for improved generalizability in causality-respecting PINNs for beam simulations Engineering Applications of Artificial Intelligence (2024)
  7. Taniya Kapoor, Hongrui Wang, Alfredo Núñez, Rolf Dollevoet. Physics-informed neural networks for solving forward and inverse problems in complex beam systems IEEE Transactions on Neural Networks and Learning Systems (2023)
  8. Taniya Kapoor, Hongrui Wang, Alfredo Núñez, Rolf Dollevoet. Physics-Informed Machine Learning for Moving Load Problems. Journal of Physics: Conference Series (2023)

Conference Papers

  1. Chinmay Datar, Taniya Kapoor, Abhishek Chandra, Qing Sun, Erik Lien Bolager, Iryna Burak, Anna Veselovska, Massimo Fornasier, Felix Dietrich. Fast Training of Accurate Physics-Informed Neural Networks without Gradient Descent. International Conference on Learning Representations (ICLR Oral) (2026)
  2. Taniya Kapoor*, Abhishek Chandra*, Daniel M. Tartakovsky, Hongrui Wang, Alfredo Núñez, Rolf Dollevoet. Neural Oscillators for Generalization of Physics-Informed Machine Learning. 38th AAAI Conference on Artificial Intelligence (2024)
  3. Taniya Kapoor, Hongrui Wang, Alfredo Núñez, Rolf Dollevoet. Predicting Traction Return Current in Electric Railway Systems through Physics-Informed Neural Networks. IEEE Symposium Series on Computational Intelligence (2022)

Peer-Reviewed Workshops

  1. Y. Shao, G. Voorn, I. Athanasiadis, Taniya Kapoor. Beyond Perfect Physics: Analysis of Physics-Informed Neural Networks under Incomplete Scientific Knowledge. KGML Bridge @ AAAI (2026)
  2. K. McKinnon, J. Speagle, J. Li, D. W. Wolla, Taniya Kapoor. Predicting Cherry Blossom Peak Bloom in Toronto through Climate-Aware Tabular Foundation Models. KGML Bridge @ AAAI (2026)
  3. E. van Tegelen, Taniya Kapoor, G. van Voorn, I. N. Athanasiadis. Late Fusion Neural Operators for Parameterized Partial Differential Equations. AI & PDE: ICLR Workshop on AI and Partial Differential Equations (2026)
  4. Taniya Kapoor, Abhishek Chandra, Daniel M. Tartakovsky, Hongrui Wang, Alfredo Núñez, Rolf Dollevoet. Neural Oscillators for Generalizing Parametric PDEs. NeurIPS Workshop on Deep Learning for Differential Equations III (2023)
  5. S. Kapoor, Abhishek Chandra, Taniya Kapoor, Mitrofan Curti. Gradient Weighted Physics-Informed Neural Networks for Capturing Shocks in Porous Media Flows. NeurIPS Workshop on Machine Learning and the Physical Sciences (2023)

Posters

  1. Taniya Kapoor, Abhishek Chandra, Daniel Tartakovsky, Hongrui Wang, Alfredo Núñez, Rolf Dollevoet. Neural oscillators for generalizing parametric PDEs. NeurIPS 2023 Workshop: The Symbiosis of Deep Learning and Differential Equations III (2023)
  2. Somiya Kapoor, Abhishek Chandra, Taniya Kapoor, Mitrofan Curti. Gradient weighted physics-informed neural networks for capturing shocks in porous media flows. NeurIPS Workshop: Machine learning and the Physical Sciences (2023).
  3. Taniya Kapoor, Hongrui Wang, Alfredo Núñez, Rolf Dollevoet. PINNs for complex beam systems. CWI Autumn School, Amsterdam(2023)
  4. Abhishek Chandra*, Taniya Kapoor*, Bram Daniels, Mitrofan Curti, Koen Tiels, Daniel M Tartakovsky, Elena A Lomonova. Neural Oscillators for Magnetic Hysteresis Modeling. CWI Autumn School, Amsterdam (2023)
  5. Taniya Kapoor, Roberto Molinaro, Siddhartha Mishra. Physics Informed Neural Networks for Approximating Fully Nonlinear PDEs. London Mathematical Society Workshop on the Mathematics of Deep Learning (2022).